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The Global Patch Collider

CVPR 2016 · Learning theory and algorithms › Prediction and decision losses · approach: Compact interpretable model classes · 38 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add multi-scale learning to the single-scale GPC (ablation)Global Patch Collider · Sintel Final, 25% recallprecision: 94.5% -> 98.1%Helped
use a multi-tree forest rather than a single tree (ablation)Global Patch Collider · Sintel Final, 25% recallprecision: 94.5% -> 93.6%Helped
add multi-scale patches (ablation)GPC sparse matching · Sintel sparse matchingendpoint error: 1.8796 -> 1.2809Helped
add motion-prior learning to multi-scale GPC (ablation)GPC sparse matching · Sintel sparse matchingmean matches: 16813 -> 26131Helped
add motion-prior learning (ablation)GPC sparse matching · IR stereo sparse matchingsparse endpoint error: 1.30 -> 1.00Helped
initialize PatchMatch with GPC correspondences instead of random initialization (ablation)PatchMatch · Dense stereo, 1 iterationerror: 1.5940 -> 1.5863Helped
train task-specific GPC on Sintel and transfer it to wide-baseline stereoGPC · Wide-baseline stereo, 1 frameEPE: 0.12 -> 0.04Helped
train task-specific GPC on Sintel and transfer it to wide-baseline stereoGPC · Wide-baseline stereo, 3 framesinlier percentage: 71% -> 53%Hurt
use motion priors with multi-scale GPC (ablation)GPC sparse matching · Sintel sparse matchingendpoint error: 1.2809 -> 1.3626Hurt

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